Quasi Maximum Likelihood for MESS Varying Coefficient Panel Data Models with Fixed Effects

The study of spatial econometrics has developed rapidly and has found wide applications in many different scientific fields,such as demog­raphy,epidemiology,regional economics,and psychology.With the deepening of research,some scholars find that there are some model specifications in spatial econometrics,such as spatial autoregressive(SAR)model and matrix exponential spatial specification(MESS),which cannot be nested within each other.Compared with the common SAR models,the MESS models have computational advantages because it eliminates the need for logarithmic determinant calculation in maxi­mum likelihood estimation and Bayesian estimation.Meanwhile,MESS models have theoretical advantages.However,the theoretical research and application of MESS models have not been promoted vigorously.Therefore,the study of MESS model theory has practical significance.This paper studies the quasi maximum likelihood estimation for ma­trix exponential spatial specification(MESS)varying coefficient panel data models with fixed effects.It is shown that the estimators of model parameters and function coefficients satisfy the consistency and asymp­totic normality to make a further supplement for the theoretical study of MESS model.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Semi-Varying Coefficient Panel Data Model with Technical Indicators Predicts Stock Returns in Financial Market

Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.

ESTIMATION IN A SEMI-VARYING COEFFICIENT MODEL FOR PANEL DATA WITH FIXED EFFECTS

This paper considers a semi-varying coefficient model for panel data with fixed effects,proposes the profile-likelihood-based estimators for the parametric and nonparametric components,and establishes convergence rates and asymptotic normality properties for both estimators.Simulation results show that the proposed estimators behave well in finite sample cases.

Panel-Data下Granger因果检验的理论和应用发展综述

Panel-Data下Granger因果检验的相关理论是最近几年才发展起来的,现有的研究提出了关于Panel-Data下Granger因果检验的四个基本假设:同质无因果关系假设(HNCH)、同质因果关系假设(HCH)、异质因果关系假设(HECH)以及异质无因果关系假设(HENCH),根据检验参数的特点给出三种类型的检验模型:固定系数模型、随机系数模型和混合固定随机系数模型。目前,还只有固定系数模型的相关理论较为完善,另外两种模型的检验还都存在一定的难度。因此,只有从理论研究和实际应用两个方面对该理论进行阐述,并对现有的理论进行简要的评述,才可指出其存在的不足及可能的改进方向。

非参数固定效应Panel Data模型的分位数回归推断

利用分位数回归方法,讨论了非参数固定效应Panel Data模型的估计和检验问题,得到了参数估计的渐近正态性及收敛速度。同时,建立一个秩得分(rank score)统计量来检验模型的固定效应,并证明了这个统计量渐近服从标准正态分布。

变系数Panel Data模型的建立与估计

研究了一类变系数Panel Data模型的建立与估计问题,并介绍了两个重要的变系数Panel Data模型.

基于空间Panel data固定效应模型的人口增长影响分析

运用空间面板数据分析方法考查了城镇化水平和人均GDP对人口增长的问题。首先介绍了面板数据在分析中的优越性,并阐述了面板数据模型的空间相关性检验。在此基础上,通过选用省级2003—2012年的Panel观测数据,对变量的面板数据进行平稳性检验,考查了变量的空间相关性。最后结合空间面板数据,运用固定效应法,估计了中国各省城镇化水平、人均GDP对人口增长的影响。经过实证检验,表明各省人口增长存在空间相关性,并概括了主要研究结论。

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

This paper applies bootstrap methods to LM tests(including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu(2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((N T)-2) and that of fast double bootstrap LM tests is O((NT)-5/2). Extensive Monte Carlo experiments suggest that,compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level.Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

Variable Selection for Fixed Effects Varying Coefficient Models

We consider the problem of variable selection for the fixed effects varying coefficient models. A variable selection procedure is developed using basis function approximations and group nonconcave penalized functions, and the fixed effects are removed using the proper weight matrices. The proposed procedure simultaneously removes the fixed individual effects, selects the significant variables and estimates the nonzero coefficient functions. With appropriate selection of the tuning parameters, an asymptotic theory for the resulting estimates is established under suitable conditions. Simulation studies are carried out to assess the performance of our proposed method, and a real data set is analyzed for further illustration.

Modelling energy efficiency in China:a fixed-effects panel stochastic frontier approach

This paper combines energy demand modelling with stochastic frontier analysis to investigate the changing trends,variations and determinants of energy efficiency for 27 Chinese provinces over the period 1995 to 2014.An aggregate‘frontier’energy demand function and an efficiency function are estimated simultaneously.We obtained several findings.First,the energy intensity is not a particularly good indicator of energy efficiency.Second,the energy efficiency levels for all the provinces improved during the sample period,but the current efficiency levels are still low,implying great potential for energy saving.In addition,the energy efficiency gap among the provinces seems to have widened over the past 20 years,as the variance has increased by almost three times.Finally,technological progress driven by new investment and the development of market mechanisms are two important drivers of energy efficiency improvement.

交通基础设施对农村居民地区间收入差距的影响

交通基础设施是助力我国全面建成小康社会的重要保障,尤其在推动我国农村经济均衡发展方面具有重要作用。为分析交通基础设施对农村居民地区间收入不平等的影响,选取我国23个省份作为研究区域,利用2010—2019年的社会经济数据,以农村居民地区间收入差距为被解释变量,公路网密度和铁路网密度为解释变量,建立固定效应模型。研究发现:公路发展会扩大地区间收入差距,铁路发展会缩小地区间收入差距;地理区位因素会对结果产生异质性影响,东部地区的铁路发展对农村居民地区间收入差距的影响并不显著。

授人以渔:公共教育支出的减贫效应及作用机制

贫困是一个世界性难题,教育扶贫是实现贫困人口彻底稳定脱贫、阻断贫困代际传递的根本途径。通过构建内生增长模型,阐释了公共教育支出减贫效应的作用机理,并基于中国内地30个省份2003—2019年的面板数据,运用固定效应模型实证检验了公共教育支出对贫困的影响机制。研究表明,公共教育支出有助于降低贫困,同时,经济发展、产业结构调整、城镇化、交通基础设施建设和人力资本积累均有利于改善贫困程度,而贸易开放的影响为负。公共教育支出的减贫效应存在显著的区域差异,并通过促进人力资本积累缓解贫困。在此基础上,从扩大教育支出规模、优化教育支出结构等方面提出了一系列提升教育扶贫效果的政策建议,针对各教育阶段实施差异化的教育支出政策,加大对中西部地区的教育投入等,为彻底阻断返贫风险,从根本上实现贫困人口可持续脱贫提供借鉴。

老龄化、财政负担与R&D投资

人口老龄化的加重会造成社会保障支出的增加,这是否会导致政府在R&D方面的支出不足?本文基于我国的省际面板数据,运用固定效应面板随机边界模型对此进行检验。研究发现:无论是采用虚拟变量法还是内转换法进行估计,人口老龄化对政府R&D支出约束部分均有显著的影响,这表明地区老龄抚养比的加重使得政府的R&D支出低于最优支出,尽管回归结果显示政府R&D支出效率较高,但依然存在个体老龄化程度不同的效率差异和区位效率差异。

数字经济是赋能城市创新的新动力吗--基于272个城市的实证研究

双循环新发展格局下,以数字经济带动城市创新是实施创新驱动发展战略和实现高质量发展的新思路。选取2015—2020年我国272个地级及以上城市面板数据,采用固定效应模型、门槛回归模型实证分析数字经济对城市创新的赋能效应。结果表明,数字经济能够显著提升城市创新力,且城市行政等级越高,数字经济对城市创新力的赋能效应越强,利用工具变量缓解内生性后,这一结果仍然显著。进一步研究表明,数字经济赋能城市创新存在多种门槛效应,城市数字经济发展水平和PE/VC活跃度越高,数字经济对城市创新力的赋能效应越显著。

福建省县级碳排放时空特征和面板数据模型分析

从中国碳核算数据库获取2000—2020年福建省县级碳排放量数据,从《福建省统计年鉴》等获取福建省县级的社会经济数据.运用地理信息系统、基于GIS的探索性空间数据分析技术、面板数据固定效应模型分析等方法,开展2000—2020年福建省碳排放的时空特征分析,构建县级碳排放量、第一产业增加值、第二产业增加值、第三产业增加值、进出口总额、公路密度、人口总量、城镇化率的面板模型.结果表明,2000—2020年福建省碳排放总体呈现增长趋势,2011年起增长速度变缓,其空间特征为闽东南方向碳排放量水平较高,闽西北方向碳排放量较低,碳排放量中心位于东南部.碳排放量的增加依次由第二产业增加值、城镇化率、人口总量、第三产业增加值、公路密度、进出口总额六要素拉动.碳排放量呈现与第一产业发展负相关的表象,分析认为在现有技术条件下通过发展第一产业持续降低碳排放量的潜力有限.提出进一步均衡福建东西部发展、持续加强节能制度和能力建设、优化能源结构的建议.

东北三省数字经济发展水平的影响因素研究

基于2017-2021年东北三省34个城市面板数据,建立固定效应面板数据模型探究其数字经济发展水平的影响因素。结果表明,经济增长、产业结构高级化、政府支出水平和电信业务收入对东北三省数字经济发展水平有较为显著的正向影响。据此提出如下对策建议:增强科学技术投入力度、加快信息通信技术传播、优化产业结构和推行差异化政策,以期为东北三省数字经济发展提供参考。

带固定效应半变系数面板数据EV模型的约束估计

研究了带固定效应的含变量误差半变系数面板数据模型的约束估计问题.首先采用Profile最小二乘方法构建出参数部分的无约束估计量,其次建立在参数部分附加约束条件下该模型的约束估计量,最后分别证明了两个估计量在适当条件下满足渐近正态分布.

半变系数固定效应面板数据模型研究PM_(2.5)与气候因素之间的动态关系

本文提出了一个半变系数固定效应面板数据模型来探索五个城市—–北京、成都、广州、上海和沈阳的PM_(2.5)与五个气候因素:累积风速、气压、露点、温度和每小时降雨量之间的动态关系,其中,PM_(2.5)是决定能见度的关键因素,对公众健康构成严重威胁.随后,我们将多元局部线性拟合、转换方法与剖面似然相结合,建立了参数向量和变系数函数向量的半参数固定效应估计.最后,展示了这五个城市2015年的估计动态关系.所提出的方法也可推广到经济和金融等其他领域的面板数据分析.

数字普惠金融、教育扩展与城乡收入不平等——基于2011-2020年省级面板数据的实证研究

长期以来,城乡二元结构的持续存在致使我国城乡之间的金融资源难以达到平衡点,且教育的不平等加剧了我国的城乡居民收入的分配差距。本文选取2011年-2020年的省级面板数据,对数字普惠金融指数、平均受教育年限、泰尔指数等指标进行收集与计算,选用固定效应模型进行回归分析。多角度实证研究表明:我国的数字普惠金融为低收入居民增强了金融服务可得性,教育扩展对城乡收入不平等的影响呈“倒U型”趋势,二者交互作用,能够收敛城乡收入差距。